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Semi-supervised learning in knowledge discovery
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DOI：10.1016/j.fss.2004.07.016.png)
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Abstract
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Recently, semi-supervised learning has received quite a lot of attention. The idea of semi-supervised learning is to learn not only from the labeled training data, but to exploit also the structural information in additionally available unlabeled data. In this paper we review existing semi-supervised approaches, and propose an evolutionary algorithm suited to learn interpretable fuzzy if-then classification rules from partially labeled data. Feasibility of our approach is shown on artificial datasets, as well as on a real-world image analysis application. (C) 2004 Published by Elsevier B.V.
Keywords:
semi-supervised learning
fuzzy classification rules
data mining
image analysis
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